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For Companies At a Glance
- Tasks: Join us to clean and analyse insurance data, influencing cyber risk insights.
- Company: Be part of a pioneering company transforming cyber risk with advanced techniques.
- Benefits: Enjoy a hybrid work model and competitive salary up to £50,000 for exceptional candidates.
- Why this job: Make a real impact in cyber risk while collaborating with experts in a dynamic environment.
- Qualifications: 2-4 years in a data-driven role with strong Python and Pandas skills required.
- Other info: Bonus points for experience in financial services or familiarity with Databricks and SQL.
Data Scientist or Junior Data Scientist – Hybrid (Bristol-based 2-3 days a week) Insurance
Are you the right candidate for this opportunity Make sure to read the full description below.
£40,000 – £45,000 (up to £50,000 for exceptional candidates)
Strong skills in Python & Pandas with at least 2 years experience in a commercial Data Driven role
Are you passionate about data and ready to make a real impact in the world of cyber risk?
We\’re working with a forward-thinking company that\’s pioneering cyber risk using advanced stochastic techniques, and they\’re on the lookout for a Data Scientist or Junior Data Scientist to join their growing team. This is a brilliant opportunity for someone with a sharp analytical mind and solid Python skills, who enjoys building efficient data pipelines and uncovering insights from complex datasets.
The Role
You\’ll play a key role in cleaning, enriching, and preparing insurance portfolio data that feeds into cutting-edge risk models. From day one, you\’ll work closely with modellers, data scientists, and engineering experts to ensure high data quality and process efficiency. The work you do will directly influence insights into cyber exposure and risk trends.
Key Responsibilities
Clean, validate, and standardise large insurance datasets using Python (especially Pandas)
Develop and refine internal tools and utilities for data cleaning workflows
Support the integration of LLMs to automate data prep, including prompt engineering and model evaluation
Generate insightful data reports related to insurance exposure and risk events
Communicate findings clearly to both technical and non-technical teams
Apply software engineering practices to improve data systems and pipelines
Continuously improve data workflows, quality, and ingestion pipelines (ETL)
Stay ahead of trends in data science, reinsurance, and cyber risk
What We\’re Looking For
2-4 years in a data-driven or technical role
Strong skills in Python and Pandas is a must
Data ingestion using ETL pipelines experience
A background in financial services is preferred, but not essential.
Insurance experience a bonus
Excellent attention to detail and a strong sense of data quality
Commercial experience blending data engineering and data science approaches
Curious, adaptable, and a natural problem solver
Bonus points for:
Experience in financial services, insurance, or reinsurance
Familiarity with Databricks, Git, PySpark or SQL
Exposure to cyber risk or large-scale modelling environments
Ready to Apply for this exciting Data Scientist role?
Send your CV to (url removed) – I\’d love to hear from you
Data Scientist / Junior Data Scientist (Python, Pandas) employer: Adecco
Contact Detail:
Adecco Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist / Junior Data Scientist (Python, Pandas)
✨Tip Number 1
Familiarise yourself with the latest trends in cyber risk and data science. This will not only help you understand the industry better but also allow you to engage in meaningful conversations during interviews, showcasing your passion and knowledge.
✨Tip Number 2
Network with professionals in the insurance and data science fields. Attend relevant meetups or webinars, and connect with people on LinkedIn. This can lead to valuable insights and potentially even referrals for the position.
✨Tip Number 3
Brush up on your Python and Pandas skills by working on personal projects or contributing to open-source projects. Having practical examples to discuss during interviews can significantly boost your chances of landing the job.
✨Tip Number 4
Prepare to discuss how you've previously cleaned and processed large datasets. Be ready to share specific examples of challenges you faced and how you overcame them, as this will demonstrate your problem-solving abilities and attention to detail.
We think you need these skills to ace Data Scientist / Junior Data Scientist (Python, Pandas)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python and Pandas, as well as any relevant projects or roles that demonstrate your data-driven skills. Use specific examples to showcase your analytical abilities and attention to detail.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data and how it relates to cyber risk. Mention your experience in cleaning and preparing datasets, and how you can contribute to the company's goals. Be sure to convey your enthusiasm for the role and the company.
Showcase Relevant Projects: If you have worked on any projects involving data pipelines, ETL processes, or data analysis, include them in your application. Briefly describe the challenges you faced and how you overcame them, focusing on the impact of your work.
Highlight Soft Skills: Don't forget to mention your soft skills, such as communication and problem-solving abilities. The role requires collaboration with both technical and non-technical teams, so demonstrating your ability to communicate findings clearly is essential.
How to prepare for a job interview at Adecco
✨Showcase Your Python and Pandas Skills
Make sure to highlight your experience with Python and Pandas during the interview. Be prepared to discuss specific projects where you've used these tools, and consider bringing examples of your work or code snippets to demonstrate your proficiency.
✨Understand the Role of Data in Cyber Risk
Familiarise yourself with how data impacts cyber risk and insurance. Research the company's approach to using data for risk modelling and be ready to discuss how your skills can contribute to their goals in this area.
✨Prepare for Technical Questions
Expect technical questions related to data cleaning, ETL processes, and data pipelines. Brush up on your knowledge of best practices in data engineering and be ready to explain your thought process when solving data-related problems.
✨Communicate Clearly with Examples
Since the role involves communicating findings to both technical and non-technical teams, practice explaining complex concepts in simple terms. Use examples from your past experiences to illustrate how you’ve effectively communicated insights in previous roles.
